Multi‐Scale Habitat Selection of Greater Caribbean Manatees in Sian Ka'an Biosphere Reserve, Mexico
Bibliographic record
Abstract
ABSTRACT Habitat selection describes population distribution as a function of environmental features. It is a fundamental process with primordial ecological and evolutionary implications. To accurately describe habitat selection, it is important to identify temporal and spatial scales perceived by the population and use these scales when modeling. Despite the growing evidence on the importance of scaling in ecology, habitat selection studies of manatees remain limited to a single spatial scale. Here, we modeled Greater Caribbean manatee ( Trichechus manatus manatus ) habitat selection in the Sian Ka'an Biosphere Reserve, Mexico, at two spatial scales: study area and 1‐km buffer. We used GPS coordinates of opportunistic encounters ( n = 102) and a pseudo‐absence approach to model manatee presence as a function of seagrass abundance, water depth, and distances to land, creeks, and seafloor depressions. To capture environmental variability, models were repeated 500 times, with each iteration using a different set of randomly generated pseudo‐absences. The probability of manatee presence increased in proximity to seafloor depressions at both scales and increased with land proximity at the large scale only. This study demonstrates the importance of multi‐scale designs in habitat selection and highlights the need for more studies looking at the ecological implications of seafloor depressions for manatees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".